Development and Evaluation of a Digital Expert Elicitation Method Aimed at Fostering Elementary School Teachers' Diagnostic Competence

Lek, Kimberley; Schoot, Rens van de

doi:10.3389/feduc.2018.00082

Cited by 10 publications

(12 citation statements)

References 47 publications

(46 reference statements)

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“…This 'translation' is called expert elicitation (see O'Hagan et al, 2006) and several so-called expert elicitation tools have been developed (see, for instance, Veen, Stoel, Zondervan-Zwijnenburg, & Van de Schoot, 2017; Zondervan-Zwijnenburg, Van de Schoot-Hubeek, Lek, Hoijtink, & Van de Schoot, 2017). We developed an online elicitation instrument specifically tailored to primary school teachers (see Lek & Van de Schoot, 2018b). With the help of this elicitation instrument, teachers translate their knowledge about the pupils into a prior in a structured, easy, and non-technical way.…”

Section: Teacher Knowledgementioning

confidence: 99%

“…In this way, the positioning of the pupilpotentiallybecomes more and more precise. Eventually, the number of puppets and the positioning of the pupil(s) using these puppets is translated into a statistical prior distribution (for technical details, see Lek & Van de Schoot, 2018b). In the app, the positioning is done as follows.…”

Section: Positioningmentioning

confidence: 99%

“…Positioning using different numbers of puppets, with five, 10, 25, and 50 puppets bottom of the screen. Note that in practice few teachers use the 50-point scale, probably because the precise positioning of students in such detail is difficult (seeLek & Van de Schoot, 2018b).…”

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confidence: 99%

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Small Sample Size Solutions

Schoot¹,

Miočević²

2020

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Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.

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Section: Teacher Knowledgementioning

confidence: 99%

Section: Positioningmentioning

confidence: 99%

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Small Sample Size Solutions

Schoot¹,

Miočević²

2020

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“…Any Bayesian model consists of at least two ingredients: a prior and a sampling model for the observed data. The prior contains the a priori beliefs about the parameter(s) and can, for instance, be based on expert knowledge, if elicitation methods are used [ 1 , 2 , 3 , 4 , 5 , 6 ]. Consecutive inferences based on the Bayesian model are built on the assumption that the (expert) prior is appropriate for the collected data.…”

Section: Introductionmentioning

confidence: 99%

How the Choice of Distance Measure Influences the Detection of Prior-Data Conflict

Lek

Schoot

2019

Entropy

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The present paper contrasts two related criteria for the evaluation of prior-data conflict: the Data Agreement Criterion (DAC; Bousquet, 2008) and the criterion of Nott et al. (2016). One aspect that these criteria have in common is that they depend on a distance measure, of which dozens are available, but so far, only the Kullback-Leibler has been used. We describe and compare both criteria to determine whether a different choice of distance measure might impact the results. By means of a simulation study, we investigate how the choice of a specific distance measure influences the detection of prior-data conflict. The DAC seems more susceptible to the choice of distance measure, while the criterion of Nott et al. seems to lead to reasonably comparable conclusions of prior-data conflict, regardless of the distance measure choice. We conclude with some practical suggestions for the user of the DAC and the criterion of Nott et al.

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“…The prior contains the a priori beliefs about the parameter(s) and can, for instance, be based on expert knowledge, if elicitation methods are used (O'Hagen et al, 2006;Kuhnert, Martin & Gri ths, 2010;O'Leary et al, 2009;Martin et al, 2011;Zondervan-Zwijnenburg, Van De Schoot-Hubeek, Lek, Hoijtink & Van De Schoot, 2017;Lek & Van De Schoot, 2018). Consecutive inferences based on the Bayesian model are built on the assumption that the (expert) prior is appropriate for the collected data.…”

Section: Introductionmentioning

confidence: 99%

Teacher knows best?

Lek¹

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Intelligence test reports usually mention a 95% confidence interval. Roughly speaking, such a confidence interval acts as a 'margin of error' with respect to the IQ score acquired by the test subject. But the exact meaning of this interval is often misunderstood. In this chapter, we brush up the use and correct interpretation of the confidence interval in intelligence tests. The examples in our exposition are based on the WISC-III NL .

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Development and Evaluation of a Digital Expert Elicitation Method Aimed at Fostering Elementary School Teachers' Diagnostic Competence

Cited by 10 publications

References 47 publications

Small Sample Size Solutions

Small Sample Size Solutions

How the Choice of Distance Measure Influences the Detection of Prior-Data Conflict

Teacher knows best?

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